Center for Transportation Research, The University of Tennessee, 600 Henley Street, Knoxville, TN 37996, USA.
Center for Transportation Research, The University of Tennessee, 600 Henley Street, Knoxville, TN 37996, USA.
Accid Anal Prev. 2014 Sep;70:320-9. doi: 10.1016/j.aap.2014.04.018. Epub 2014 May 17.
Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types.
通过警方报告收集碰撞数据，并与道路清单数据集成进行进一步分析。整合后的警方报告和清单数据为道路实体（例如路段或交叉口）提供相关的多元数据。对这些数据的分析揭示了重要的关系，有助于关注高风险情况并提出安全对策。为了了解碰撞频率与相关变量之间的关系，同时充分利用可用数据，多元随机参数模型是合适的，因为它们可以同时考虑特定碰撞类型之间的相关性，并考虑未观察到的异质性。然而，与相关多元数据相关的一个关键问题是，由于碰撞次数有很多类别，无碰撞样本数量增加。本文描述了一种用于联合建模碰撞次数的多元随机参数零膨胀负二项式（MRZINB）回归模型。采用全贝叶斯方法估计模型参数。分析了田纳西州城市信号交叉口的碰撞频率。本文研究了 MZINB 和 MRZINB 回归模型在建立碰撞频率、路面状况、交通因素和道路交叉口几何设计特征之间关系方面的性能。与 MZINB 模型相比，MRZINB 模型确定了更多具有统计学意义的因素，并在建立关系方面提供了更好的拟合优度。实证结果表明，MRZINB 模型在适应相关数据中的未观察到的异质性和多余零计数方面具有大多数理想的统计特性。值得注意的是，在随机参数 MZINB 模型中，不同碰撞类型的交叉口的估计参数差异很大。
